Related Experiment Video
Updated: Dec 31, 2025

14:01
Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition
Published on: May 22, 2015
43.2K
Overcoming randomness does not rule out the importance of inherent randomness for functionality
1Department of Medicine, Hadassah-Hebrew University Medical Center, Jerusalem, Israel, ilan@hadassah.org.il.
Journal of Biosciences
|January 3, 2020
Summary
Randomness is inherent in nature and can be a beneficial disorder, not always a negative one. Understanding and acknowledging randomness is key to appreciating its role in functionality and organ processes.
Area of Science:
- Explores the complex role of randomness across various scientific disciplines.
Background:
- Natural processes frequently exhibit randomness.
- Disorders arising from randomness are not always detrimental to functionality.
- Examples of managing randomness exist across diverse fields.
Purpose of the Study:
- To re-evaluate the perception of randomness in natural and biological systems.
- To highlight the significance of randomness in functional processes.
- To propose a paradigm shift in viewing randomness as potentially beneficial.
Main Methods:
- Review of existing literature and case studies across multiple scientific domains.
- Analysis of examples where randomness is managed or integrated.
- Conceptual framework development for understanding 'beneficial disorder'.
Main Results:
- Randomness, while sometimes a disorder, is integral to many natural phenomena.
- Suppression or overcoming randomness is not always necessary or optimal.
- Randomness can represent a higher level of functionality, termed 'beneficial disorder'.
Conclusions:
- Randomness should be acknowledged and understood, not merely suppressed.
- The concept of 'beneficial disorder' offers a new perspective on randomness in biological and natural systems.
- Further research into harnessing beneficial disorder could unlock new functional potentials.
More Related Videos
Related Concept Videos
Random Error
7.6K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
7.6K
Random and Systematic Errors
14.2K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
14.2K
Randomized Experiments
8.8K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
8.8K
Propagation of Uncertainty from Random Error
1.6K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.6K
Random Variables
17.1K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
17.1K
Uncertainty in Measurement: Accuracy and Precision
99.1K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
99.1K

